G85R vs WT Females

##GO.Sim.PCA function

##GO20 Function

##KEGG20 Function

BP.GO.fig

CC.GO.fig

MF.GO.fig

KEGG.fig

heatmapPlot(simMatrix,
            reducedTerms,
            annotateParent=TRUE,
            annotationLabel="parentTerm",
            fontsize=6,
            main = "Repressor Gene Enriched Biological Process GO terms")

use.data   = simMatrix
GO.Sim.PCA(use.data)

treemapPlot(reducedTerms)

GRxWT Df

BP.GO.fig

CC.GO.fig

MF.GO.fig

KEGG.fig

GRxWT OE

BP.GO.fig

CC.GO.fig

MF.GO.fig

KEGG.fig

heatmapPlot(simMatrix,
            reducedTerms,
            annotateParent=TRUE,
            annotationLabel="parentTerm",
            fontsize=6,
            main = "Repressor Gene Enriched Biological Process GO terms")

use.data   = simMatrix
GO.Sim.PCA(use.data)

treemapPlot(reducedTerms)

Comparing the three

data = data.frame(Condition = c("Control", "Defficient", "Over Expression"), DEGs = c(length(C.sigs), length(Df.sigs), length(OE.sigs)))

fig <- plot_ly(data, 
               x = ~Condition, y = ~DEGs, 
               type = 'bar',
               name = ~Condition,
               color = ~Condition,
               colors = c("coral3","gold2", "steelblue3"),
               marker = list(line = list(color = "black", width = 1.5)))

fig

##Triple Venn
grid.newpage()
draw.triple.venn(area1=length(s1),area2=length(s2),area3=length(s3),n12=length(intersect(s1,s2)),n23 = length(intersect(s2,s3)),n13 = length(intersect(s1,s3)),n123 = length(intersect(intersect(s1,s2),s3)),category=c(set1,set2,set3),fill = c("brown1","gold2", "steelblue2"),cex=1.5,cat.cex = 1)

## (polygon[GRID.polygon.529], polygon[GRID.polygon.530], polygon[GRID.polygon.531], polygon[GRID.polygon.532], polygon[GRID.polygon.533], polygon[GRID.polygon.534], text[GRID.text.535], text[GRID.text.536], text[GRID.text.537], text[GRID.text.538], text[GRID.text.539], text[GRID.text.540], text[GRID.text.541], text[GRID.text.542], text[GRID.text.543], text[GRID.text.544])